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An Improved Artificial Bee Colony Algorithm with a Probabilistic Crossover and Lock Mechanism.

Zeynep Haber1, Harun Uguz1, Huseyin Hakli2

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This study enhances the Artificial Bee Colony (ABC) algorithm with crossover and lock mechanisms for complex resource allocation. The improved ABC framework achieves superior cost reduction and solution feasibility in liquid transportation logistics.

Keywords:
artificial bee colonycrossover operatorliquid transportationlock mechanismmulti-resource allocation

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Area of Science:

  • Optimization Algorithms
  • Logistics Management
  • Computational Intelligence

Background:

  • The Artificial Bee Colony (ABC) algorithm, a population-based optimization method, faces challenges with convergence stability and local refinement in discrete, constrained problems.
  • Existing ABC algorithms lack tailored solutions for integrated multi-resource allocation in complex logistical scenarios.

Purpose of the Study:

  • To enhance the Artificial Bee Colony (ABC) algorithm's performance for discrete and constrained optimization problems.
  • To develop an improved ABC framework integrating a probabilistic Uniform crossover and a gene-level lock mechanism.
  • To address the integrated multi-resource allocation problem in liquid transportation, optimizing driver, truck, trailer, and ISO tank assignments.

Main Methods:

  • Integration of a probabilistic Uniform crossover operator into the ABC framework.
  • Incorporation of a gene-level lock mechanism to improve local search and convergence.
  • Application and testing of the enhanced ABC framework on an integrated multi-resource allocation problem in liquid transportation.

Main Results:

  • The combined Uniform crossover and lock mechanism significantly reduced mean costs (14.94) compared to individual mechanisms or baseline ABC.
  • The proposed ABC configuration consistently yielded the lowest mean costs across various dataset sizes.
  • The enhanced method outperformed established metaheuristics and manual planning, achieving lower costs and feasible solutions.

Conclusions:

  • The proposed enhanced ABC framework demonstrates improved convergence stability and local refinement capabilities.
  • The integration of Uniform crossover and a gene-level lock mechanism provides a synergistic effect for optimization.
  • The method offers a robust and practically relevant solution for complex integrated resource allocation problems in logistics.